Sensory event-related potential morphology predicts age in premature infants

Abstract

Objective: We investigated whether sensory-evoked cortical potentials could be used to estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment.

Methods: Infants aged between 28- and 40-weeks post-menstrual age (PMA) (166 recording sessions in 96 infants) received trains of visual and tactile stimuli. Neurodynamic response functions for each stimulus were derived using principal component analysis and a machine learning model trained and validated to predict infant age.

Results: PMA could be predicted accurately from the magnitude of the evoked responses (training set mean absolute error and 95% confidence intervals: 1.41 [1.14; 1.74] weeks,p = 0.0001; test set mean absolute error: 1.55 [1.21; 1.95] weeks,p = 0.0002). Moreover, we show that their predicted age (their brain age) is correlated with a measure known to relate to maturity of the nervous system and is linked to long-term neurodevelopment.

Conclusions: Sensory-evoked potentials are predictive of age in premature infants and brain age deviations are related to biologically and clinically meaningful individual differences in nervous system maturation.

Significance: This model could be used to detect abnormal development of infants' response to sensory stimuli in their environment and may be predictive of neurodevelopmental outcome.

Keywords: Electroencephalography; Machine learning; Preterm; Reflex withdrawal; Tactile; Visual.